With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy...With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.展开更多
In Wireless Sensor Networks(WSNs),survivability is a crucial issue that is greatly impacted by energy efficiency.Solutions that satisfy application objectives while extending network life are needed to address severe ...In Wireless Sensor Networks(WSNs),survivability is a crucial issue that is greatly impacted by energy efficiency.Solutions that satisfy application objectives while extending network life are needed to address severe energy constraints inWSNs.This paper presents an Adaptive Enhanced GreyWolf Optimizer(AEGWO)for energy-efficient cluster head(CH)selection that mitigates the exploration–exploitation imbalance,preserves population diversity,and avoids premature convergence inherent in baseline GWO.The AEGWO combines adaptive control of the parameter of the search pressure to accelerate convergence without stagnation,a hybrid velocity-momentum update based on the dynamics of PSO,and an intelligent mutation operator to maintain the diversity of the population.The search is guided by a multi-objective fitness,which aims at maximizing the residual energy,equal distribution of CH,minimizing the intra-cluster distance,desirable proximity to sinks,and enhancing the coverage.Simulations on 100 nodes homogeneousWSN Tested the proposed AEGWO under the same conditions with LEACH,GWO,IGWO,PSO,WOA,and GA,AEGWO significantly increases stability and lifetime compared to LEACHand other tested algorithms;it has the best first,half,and last node dead,and higher residual energy and smaller communication overhead.The findings prove that AEGWO provides sustainable energy management and better lifetime extension,which makes it a robust,flexible clustering protocol of large-scaleWSNs.展开更多
Qingke,a staple crop grown on the high-altitude Tibetan Plateau,has evolved a metabolomic profile providing both environmental stress resilience and human nutrition.We review the hypothesis that the metabolites that c...Qingke,a staple crop grown on the high-altitude Tibetan Plateau,has evolved a metabolomic profile providing both environmental stress resilience and human nutrition.We review the hypothesis that the metabolites that confer cold and UV resistance on the crop also facilitate human adaptation to high-altitude stresses.Specifically,β-glucans regulate blood glucose primarily via short-chain fatty acids(SCFAs)produced through gut microbiota fermentation,which directly mediate glucose homeostasis.Phenolamides accumulate via the phenylpropanoid pathway,with chalcone isomerase(CHI)serving as a key enzyme in flavonoid biosynthesis and enhancing UV-B resistance.Under low temperatures,β-glucans improve frost tolerance by modulating osmotic balance and inhibiting ice-nucleating proteins,while lipids maintain membrane fluidity to sustain cellular function during cold stress.Importantly,we explore the hypothesis that these same metabolites,upon consumption,may facilitate human adaptation to high-altitude stresses.This hypothesis is supported by preliminary epidemiological associations between Qingke consumption and favorable health outcomes in high-altitude populations,as well as established bioactivities of the implicated metabolites in vitro and in animal models.However,direct causal evidence in humans and a comprehensive understanding of the underlying molecular mechanisms remain key knowledge gaps that warrant future investigation.Qingke as a unique resource at the interface of agricultural resilience and human nutrition.Understanding its metabolic blueprint will inform the development of functional foods and climate-resilient crops.展开更多
It is essential to understand how adaptation needs and options differ among stakeholders in protected areas(PAs)to effectively implement climate change(CC)adaptation strategies.Using the Qiangtang PA in Xizang as a ca...It is essential to understand how adaptation needs and options differ among stakeholders in protected areas(PAs)to effectively implement climate change(CC)adaptation strategies.Using the Qiangtang PA in Xizang as a case study,this research examines CC adaptation needs and options from the perspectives of stakeholders across multiple administrative levels,including provincial,prefectural,county authorities,73 protection stations,and 13364 pastoralists residing within the PA.The findings show that stakeholders at the provincial level,as well as those from the Ali and Naqu prefectures and six counties,place greater emphasis on institutional and resource-related needs than on other categories(attention score:7.0-9.3 vs.5.0-7.0).In contrast,stakeholders from the 73 protection stations prioritize technological and capacity-building needs more strongly than other types(attention score:8.0-9.0 vs.4.0-8.0).The 13364 pastoralists assign the highest importance to social needs relative to other categories(attention score:9.0-9.5 vs.3.0-8.0).Most of the eight existing protection measures were found to indirectly support broader climate adaptation efforts.In particular,protective actions addressing fire,pests,and weather-related disasters can be classified as autonomous adaptation,while other measures generate outcomes that enhance adaptation capacity under specific conditions.Adaptation options,grouped into three main types and 13 subcategories,differ across stakeholder groups,although substantial overlap exists between these options and current protective actions,including ecosystem based adaptation strategies,adaptation-related practices,autonomous adaptation measures,and emergency interventions.Overall,these findings highlight the critical role of all stakeholders-especially staff from the 73 protection stations and the 13364 pastoralists-in the effective implementation of adaptation actions within the PA.展开更多
Climate change poses a profound threat to mountain agro-ecosystems,particularly in the Himalayan region of West Bengal,India,by disrupting precipitation patterns,increasing temperature variability,and intensifying ext...Climate change poses a profound threat to mountain agro-ecosystems,particularly in the Himalayan region of West Bengal,India,by disrupting precipitation patterns,increasing temperature variability,and intensifying extreme weather events.Despite growing evidence of climate change impacts,there remains a critical research gap in understanding how socioeconomic factors drive farmers' adaptation strategies to climate change in this vulnerable region.This study examines how farmers in the Himalayan region of West Bengal,India,perceived and responded to the growing impacts of climate change on mountain agro-ecosystems.Drawing on cross-sectional data from 370 farm households selected through multistage sampling,the research employs a combination of analytical tools,including the severity index(SI) to assess farmers' perceptions to climate change,the adaptation index(AI) to evaluate adaptive responses,the Garrett's ranking technique to prioritize constraints,and the ordered logistic regression to identify key socioeconomic drivers of adaptation.Findings reveal a high level of climate awareness among farmers,particularly regarding the increase in weather extremes(SI=74.87%),increase in temperature(SI=72.31%),and irregular rainfall patterns and highly erratic rainfall(SI=62.52%).The most commonly adopted strategies include adopting intercropping and mixed cropping systems(AI=0.613),adoption of the integrated farming system model(AI=0.600),and shift towards non-farm employment(AI=0.608),while the adoption of climate-resilient crop varieties and improved irrigation remains limited.Regression analysis highlights that education(regression coefficient=0.38),average landholding size(regression coefficient=1.21),and access to daily weather forecast information(regression coefficient=1.92) significantly promote adaptive behaviour,whereas age(regression coefficient= –0.09) and gender(regression coefficient= –0.76) are negatively associated.Institutional constraints,particularly unavailability of institutional credit,emerge as primary barriers.The study underscores the urgent need for region-specific,inclusive policy frameworks that enhance climate advisory services,support technology dissemination,and empower marginalized groups in the Himalayan region of West Bengal.By fostering informed,equitable,and resilient agricultural systems,these strategies can significantly strengthen the adaptive capacity of mountain farming communities and contribute to sustainable development under a changing climate.展开更多
1.Introduction The field of exercise science is experiencing a renaissance,with recent research illuminating the molecular,cellular,and systemic effects of physical activity.This is largely due to the now unequivocal ...1.Introduction The field of exercise science is experiencing a renaissance,with recent research illuminating the molecular,cellular,and systemic effects of physical activity.This is largely due to the now unequivocal evidence that a lack of physical activity,not only has direct effects on the prevalence of non-contagious diseases(NCDs)but has profound additive effects of other risk factors for NCD such as obesity and hypertension.1 The articles in this special topic of Journal of Sport and Health Science(JSHS)are dedicated to research on Exercise biochemistry&metabolism.展开更多
The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduce...The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.展开更多
Gibbons are small,arboreal apes that play a critical role in tropical biodiversity and ecosystem ecology.However,nearly all species of gibbons are threatened by habitat loss,illegal trade,hunting,and other human activ...Gibbons are small,arboreal apes that play a critical role in tropical biodiversity and ecosystem ecology.However,nearly all species of gibbons are threatened by habitat loss,illegal trade,hunting,and other human activities.Long-term poor understanding of their genetics and evolution undermines effective conservation efforts.In this study,we analyse comparative population genomic data of four Nomascus species.Our results reveal strong genetic differentiation and gene flow among Nomascus species.Additionally,we identify genomic features that are potentially related to natural selection linked to vocalization,fructose metabolism,motor balance,and body size,consistent with the unique phenotype and adaptability of gibbons.Inbreeding,coupled with population declines due to climate change and historical human activities,leads to reduced genetic diversity and the accumulation of deleterious variations that likely affect cardiovascular disease and the reproductive potential of gibbons and further reduce their fitness,highlighting the urgent need for effective conservation strategies.展开更多
Objective:International students frequently face psychological adaptation difficulties while studying and living abroad.As an effective psychological resource,positive solitude has been identified as a potential facto...Objective:International students frequently face psychological adaptation difficulties while studying and living abroad.As an effective psychological resource,positive solitude has been identified as a potential factor for improving psychological well-being,but the underlying mechanism linking the two has not been fully explored.The current study aims to explore the relationship between positive solitude and psychological adaptation of international students,with particular emphasis on the intermediary roles of authenticity and loneliness.Methods:A total of 529 international tertiary students(Mage=23.76,SD=5.08;60.68%male)were surveyed using the Positive Solitude Scale(PSS),Authenticity Scale(AS),6-item UCLA Loneliness Scale(ULS-6),and Brief Psychological Adaptation Scale(BPAS).SPSS27.0 was used for descriptive statistical analysis and Pearson correlation analysis.PROCESS macro(Model 6)was employed to test a serial mediation model,in which authenticity and loneliness function as intermediary variables between positive solitude and psychological adaptation.Results:The correlation analysis indicated significant associations among positive solitude,authenticity,loneliness,and psychological adaptation(r=−0.544~0.511).Positive solitude was directly and positively related to psychological adaptation(β=0.132,t=3.609,p<0.001)and indirectly related to psychological adaptation through two pathways:a single mediation via authenticity(indirect effect=0.089)and a serial mediation through authenticity and loneliness(indirect effect=0.062).Loneliness did not serve as a significant mediator(indirect effect=–0.015,95%CI[–0.049,0.019]).The total indirect effect was 0.136.Conclusions:Interventions targeting international students’capacity for experiencing positive solitude and authenticity can help to reduce loneliness and increase psychological adaptation.The findings derived from this study are conducive to understanding the relationship between positive solitude and psychological adaptation,as well as its underlying mechanisms.In addition,the study offers a new perspective for educational management and psychological counseling services for international students.展开更多
Conventionally,foundations have been classified as shallow or deep in routine civil engineering practice.However,due to recent developments,two other approaches,semi-deep and ground modification foundations,are now av...Conventionally,foundations have been classified as shallow or deep in routine civil engineering practice.However,due to recent developments,two other approaches,semi-deep and ground modification foundations,are now available,complicating foundation categorization.Accordingly,a new concept for foundation categorization is introduced in this paper based on insights into the theory of structure analysis.Based on the form aspect,foundation systems can be categorized as one-dimensional(linear),two-dimensional(planar),and threedimensional(volumetric).Based on the load transfer aspect,foundations can also be categorized as vector-acting(piles),section or surface-acting(rafts and shells),and block-acting(piled rafts).As a step toward implementing this new categorization scheme,a database of 22 cases has been compiled,symbolizing novel introduced foundation systems.This compilation involves structures such as offshore jackets,high-rise buildings,towers and storages,and diverse geomaterials.Among them,a few have been selected for detailed evaluation,emphasizing influential factors in foundation selection,comprising superstructure,subsoil condition,foundation system,circumferential conditions,and supplementary considerations,that is,constructional and sustainability-based issues.Lessons learned from experience and these knowledge-based cases have described for foundation selection and implementation.Geotechnical and practical aspects with critical components have been realized as major performance assessment and comparison factors.Foundation systems have been compared and ranked using the improved analytic hierarchy process approach.Finally,four categories of buildings,from low-rise to towers and four prevailing levels of soil strength,from soft to very hard,have been considered to propose a perspective for building substructure implementation,adapted via relevant cases.Overall,the introduced categorization is recognized as an efficient algorithm for the experimentation of appropriate foundations for specific structures and subsoil conditions.展开更多
Nitric oxide(NO)is a key vasodilator that regulates vascular pressure and blood flow.Tibetans have developed a"blunted"mechanism for regulating NO levels at high altitude,with GTP cyclohydrolase 1(GCH1)ident...Nitric oxide(NO)is a key vasodilator that regulates vascular pressure and blood flow.Tibetans have developed a"blunted"mechanism for regulating NO levels at high altitude,with GTP cyclohydrolase 1(GCH1)identified as a key candidate gene.Here,we present comprehensive genetic and functional analyses of GCH1,which exhibits strong Darwinian positive selection in Tibetans.We show that Tibetan-enriched GCH1 variants down-regulate its expression in the blood of Tibetans.Based on this observation,we generate the heterozygous Gch1 knockout(Gch1^(+/-))mouse model to simulate its downregulation in Tibetans.We find that under prolonged hypoxia,the Gch1^(+/-)mice have relatively higher blood NO and blood oxygen saturation levels compared with the wild-type(WT)controls,providing better oxygen supplies to the cardiovascular and pulmonary systems.Markedly,hypoxia-induced cardiac hypertrophy and pulmonary remodeling are significantly attenuated in the Gch1^(^(+/-))mice compared with the WT controls,likely due to the adaptive changes in molecular regulations related to metabolism,inflammation,circadian rhythm,extracellular matrix,and oxidative stress.This study sheds light on the role of GCH1 in regulating blood NO,contributing to the physiological adaptation of the cardiovascular and pulmonary systems in Tibetans at high altitude.展开更多
To explore the adaptive mechanisms of the partial nitritation-anammox(PNA)process under high salinity stress during kitchen wastewater treatment,focusing on their physiological and molecular responses through metageno...To explore the adaptive mechanisms of the partial nitritation-anammox(PNA)process under high salinity stress during kitchen wastewater treatment,focusing on their physiological and molecular responses through metagenomic analysis.An airlift inner-circulation partition bioreactor(AIPBR)was developed,featuring an inner cylinder and a flow guide tube to create distinct oxygen gradients,facilitating the study of microbial adaptation under varying salt conditions.The AIPBR was operated with synthetic wastewater containing ammonium concentrations of 1800±100 mg/L and salinity gradients ranging from 1 to 10 g/L,followed by a fixed salinity period at 6 g/L,with ammonium concentrations approximately 850 mg/L.High-throughput metagenomic analysis revealed shifts in functional genes and metabolic pathways in response to salinity stress.Anammox bacteria adapted by enriching genes involved in the synthesis of osmoprotective compounds and activating energy-producing pathways like the tricarboxylic acid cycle(TCA).These adaptations,along with modifications in membrane composition,were essential for sustaining system stability under elevated salinity.Under prolonged high salinity stress,anaerobic ammonium oxidizing(AnAOB)exhibited improved salt tolerance,maintaining a total nitrogen removal efficiency above 85%and stabilizing after an adaptation phase.The metagenomic data revealed a marked enrichment of genes associated with ion transport,stress response mechanisms,and DNA repair pathways.Changes in microbial community composition favored salt-tolerant species,supporting system stability.These findings highlight the applicability of the developed bioreactor for scaling up the PNA process to handle high-salinity wastewater,providing a promising avenue for sustainable nitrogen removal in challenging environments.展开更多
High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of ...High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of labels.Moreover,an optimization problem that fully considers all dependencies between features and labels is difficult to solve.In this study,we propose a novel regression-basedmulti-label feature selectionmethod that integrates mutual information to better exploit the underlying data structure.By incorporating mutual information into the regression formulation,the model captures not only linear relationships but also complex non-linear dependencies.The proposed objective function simultaneously considers three types of relationships:(1)feature redundancy,(2)featurelabel relevance,and(3)inter-label dependency.These three quantities are computed usingmutual information,allowing the proposed formulation to capture nonlinear dependencies among variables.These three types of relationships are key factors in multi-label feature selection,and our method expresses them within a unified formulation,enabling efficient optimization while simultaneously accounting for all of them.To efficiently solve the proposed optimization problem under non-negativity constraints,we develop a gradient-based optimization algorithm with fast convergence.Theexperimental results on sevenmulti-label datasets show that the proposed method outperforms existingmulti-label feature selection techniques.展开更多
Community-pioneered Nature-based Solutions(NbS)have become the main strategies in climate adaptation,although the evidence of their effectiveness and the governing conditions is still fragmented across hazards,ecosyst...Community-pioneered Nature-based Solutions(NbS)have become the main strategies in climate adaptation,although the evidence of their effectiveness and the governing conditions is still fragmented across hazards,ecosystems,and disciplines.The current review is a synthesis of the worldwide empirical research based on the concept of community-led NbS,meaning those interventions where communities have significant decision-making power and responsibility concerning the design,stewardship,sharing of benefits,and learning.On a taxonomy that differentiates between proximal ecosystem functionality and hazard modulation and distal human vulnerability reduction,and procedural,distributional,and recognition justice,we systematize the evidence-based findings according to hazardecosystem-intervention type(coastal storms and sea-level rise,flooding,drought and water insecurity,urban heat,and emerging compound risks)and we compare the outcomes.The results are reported to have the co-benefits of biodiversity gain,livelihood diversification,and better well-being,though they can be neutralized by elite capture,exclusion,tenure insecurity,as well as,in cities,green gentrification and displacement.The analysis of governance indicates repeating bundles related to longer-lasting and fairer results:hedge rights and tenure,community-enforceable and legitimizing representation institutions,financing institutions with longer horizons of maintenance and active adaptation,protection,and grievance,ethical supervision,and data governance.Our findings conclude that to scale community-led NbS,we need to switch the targets of areas to the target of governance quality and design of evaluation that would connect a change in the ecosystem to lived risk reduction and distributional change.展开更多
Populus species,important economic species combining rapid growth with broad ecological adaptability,play a critical role in sustainable forestry and bioenergy production.In this study,we performed whole-genome resequ...Populus species,important economic species combining rapid growth with broad ecological adaptability,play a critical role in sustainable forestry and bioenergy production.In this study,we performed whole-genome resequencing of 707 individuals from a full-sib family to develop comprehensive single nucleotide polymorphism(SNP)markers and constructed a high-density genetic linkage map of 19 linkage groups.The total genetic length of the map reached 3623.65 cM with an average marker interval of 0.34 cM.By integrating multidimensional phenotypic data,89 quantitative trait loci(QTL)associated with growth,wood physical and chemical properties,disease resistance,and leaf morphology traits were identified,with logarithm of odds(LOD)scores ranging from 3.13 to 21.72 Notably,pleiotropic analysis revealed significant colocaliza and phenotypic variance explained between 1.7% and 11.6%.-tion hotspots on chromosomes LG1,LG5,LG6,LG8,and LG14,with epistatic interaction network analysis confirming genetic basis of coordinated regulation across multiple traits.Functional annotation of 207 candidate genes showed that R2R3-MYB and bHLH transcription factors and pyruvate kinase-encoding genes were significantly enriched,suggesting crucial roles in lignin biosynthesis and carbon metabolic pathways.Allelic effect analysis indicated that the frequency of favorable alleles associated with target traits ranged from 0.20 to 0.55.Incorporation of QTL-derived favorable alleles as random effects into Bayesian-based genomic selection models led to an increase in prediction accuracy ranging from 1% to 21%,with Bayesian ridge regression as the best predictive model.This study provides valuable genomic resources and genetic insights for deciphering complex trait architecture and advancing molecular breeding in poplar.展开更多
Quantile regression(QR)has become an important tool to measure dependence of response variable's quantiles on a number of predictors for heterogeneous data,especially heavy-tailed data and outliers.However,it is q...Quantile regression(QR)has become an important tool to measure dependence of response variable's quantiles on a number of predictors for heterogeneous data,especially heavy-tailed data and outliers.However,it is quite challenging to make statistical inference on distributed high-dimensional QR with missing data due to the distributed nature,sparsity and missingness of data and nondifferentiable quantile loss function.To overcome the challenge,this paper develops a communicationefficient method to select variables and estimate parameters by utilizing a smooth function to approximate the non-differentiable quantile loss function and incorporating the idea of the inverse probability weighting and the penalty function.The proposed approach has three merits.First,it is both computationally and communicationally efficient because only the first-and second-order information of the approximate objective function are communicated at each iteration.Second,the proposed estimators possess the oracle property after a limited number of iterations without constraint on the number of machines.Third,the proposed method simultaneously selects variables and estimates parameters within a distributed framework,ensuring robustness to the specified response probability or propensity score function of the missing data mechanism.Simulation studies and a real example are used to illustrate the effectiveness of the proposed methodologies.展开更多
Rheumatoid arthritis(RA)patients face significant psychological challenges alongside physical symptoms,necessitating a comprehensive understanding of how psychological vulnerability and adaptation patterns evolve thro...Rheumatoid arthritis(RA)patients face significant psychological challenges alongside physical symptoms,necessitating a comprehensive understanding of how psychological vulnerability and adaptation patterns evolve throughout the disease course.This review examined 95 studies(2000-2025)from PubMed,Web of Science,and CNKI databases including longitudinal cohorts,randomized controlled trials,and mixed-methods research,to characterize the complex interplay between biological,psychological,and social factors affecting RA patients’mental health.Findings revealed three distinct vulnerability trajectories(45%persistently low,30%fluctuating improvement,25%persistently high)and four adaptation stages,with critical intervention periods occurring 3-6 months postdiagnosis and during disease flares.Multiple factors significantly influence psychological outcomes,including gender(females showing 1.8-fold increased risk),age(younger patients experiencing 42%higher vulnerability),pain intensity,inflammatory markers,and neuroendocrine dysregulation(48%showing cortisol rhythm disruption).Early psychological intervention(within 3 months of diagnosis)demonstrated robust benefits,reducing depression incidence by 42%with effects persisting 24-36 months,while different modalities showed complementary advantages:Cognitive behavioral therapy for depression(Cohen’s d=0.68),mindfulness for pain acceptance(38%improvement),and peer support for meaning reconstruction(25.6%increase).These findings underscore the importance of integrating routine psychological assessment into standard RA care,developing stage-appropriate interventions,and advancing research toward personalized biopsychosocial approaches that address the dynamic psychological dimensions of the disease.展开更多
The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects acc...The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS.展开更多
Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional ...Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional domain adaptation methods assume a single source domain,making them less suitable for modern deep learning settings that rely on diverse and large-scale datasets.To address this limitation,recent research has focused on Multi-Source Domain Adaptation(MSDA),which aims to learn effectively from multiple source domains.In this paper,we propose Efficient Domain Transition for Multi-source(EDTM),a novel and efficient framework designed to tackle two major challenges in existing MSDA approaches:(1)integrating knowledge across different source domains and(2)aligning label distributions between source and target domains.EDTM leverages an ensemble-based classifier expert mechanism to enhance the contribution of source domains that are more similar to the target domain.To further stabilize the learning process and improve performance,we incorporate imitation learning into the training of the target model.In addition,Maximum Classifier Discrepancy(MCD)is employed to align class-wise label distributions between the source and target domains.Experiments were conducted using Digits-Five,one of the most representative benchmark datasets for MSDA.The results show that EDTM consistently outperforms existing methods in terms of average classification accuracy.Notably,EDTM achieved significantly higher performance on target domains such as Modified National Institute of Standards and Technolog with blended background images(MNIST-M)and Street View House Numbers(SVHN)datasets,demonstrating enhanced generalization compared to baseline approaches.Furthermore,an ablation study analyzing the contribution of each loss component validated the effectiveness of the framework,highlighting the importance of each module in achieving optimal performance.展开更多
To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervis...To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.展开更多
文摘With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.
基金The Open Access publication fee for this article was fully covered by Abu Dhabi University.
文摘In Wireless Sensor Networks(WSNs),survivability is a crucial issue that is greatly impacted by energy efficiency.Solutions that satisfy application objectives while extending network life are needed to address severe energy constraints inWSNs.This paper presents an Adaptive Enhanced GreyWolf Optimizer(AEGWO)for energy-efficient cluster head(CH)selection that mitigates the exploration–exploitation imbalance,preserves population diversity,and avoids premature convergence inherent in baseline GWO.The AEGWO combines adaptive control of the parameter of the search pressure to accelerate convergence without stagnation,a hybrid velocity-momentum update based on the dynamics of PSO,and an intelligent mutation operator to maintain the diversity of the population.The search is guided by a multi-objective fitness,which aims at maximizing the residual energy,equal distribution of CH,minimizing the intra-cluster distance,desirable proximity to sinks,and enhancing the coverage.Simulations on 100 nodes homogeneousWSN Tested the proposed AEGWO under the same conditions with LEACH,GWO,IGWO,PSO,WOA,and GA,AEGWO significantly increases stability and lifetime compared to LEACHand other tested algorithms;it has the best first,half,and last node dead,and higher residual energy and smaller communication overhead.The findings prove that AEGWO provides sustainable energy management and better lifetime extension,which makes it a robust,flexible clustering protocol of large-scaleWSNs.
基金supported by the Financial Special Fund,grant number XZ202401JD0027National Barley Industry Technology System(CARS-05-01A-08)+3 种基金the Xizang Agri-Tech Innovation Project(XZNKY-2025-CXGC-T01)the Joint Funds of the National Natural Science Foundation of China(No.U20A2026)the Financial Special Fund,grant number(32401784,2017CZZX001/2,XZNKY-2018-C-021 and NYSTC202401)the China Agriculture Research System of Barley(CARS-05).
文摘Qingke,a staple crop grown on the high-altitude Tibetan Plateau,has evolved a metabolomic profile providing both environmental stress resilience and human nutrition.We review the hypothesis that the metabolites that confer cold and UV resistance on the crop also facilitate human adaptation to high-altitude stresses.Specifically,β-glucans regulate blood glucose primarily via short-chain fatty acids(SCFAs)produced through gut microbiota fermentation,which directly mediate glucose homeostasis.Phenolamides accumulate via the phenylpropanoid pathway,with chalcone isomerase(CHI)serving as a key enzyme in flavonoid biosynthesis and enhancing UV-B resistance.Under low temperatures,β-glucans improve frost tolerance by modulating osmotic balance and inhibiting ice-nucleating proteins,while lipids maintain membrane fluidity to sustain cellular function during cold stress.Importantly,we explore the hypothesis that these same metabolites,upon consumption,may facilitate human adaptation to high-altitude stresses.This hypothesis is supported by preliminary epidemiological associations between Qingke consumption and favorable health outcomes in high-altitude populations,as well as established bioactivities of the implicated metabolites in vitro and in animal models.However,direct causal evidence in humans and a comprehensive understanding of the underlying molecular mechanisms remain key knowledge gaps that warrant future investigation.Qingke as a unique resource at the interface of agricultural resilience and human nutrition.Understanding its metabolic blueprint will inform the development of functional foods and climate-resilient crops.
基金supported by the National Key Research and Development Project[Grand No.2022YFF0802304]Key Research and Development and Transformation Project of the Xizang Autonomous Region[Grand No.XZ202501ZY0119].
文摘It is essential to understand how adaptation needs and options differ among stakeholders in protected areas(PAs)to effectively implement climate change(CC)adaptation strategies.Using the Qiangtang PA in Xizang as a case study,this research examines CC adaptation needs and options from the perspectives of stakeholders across multiple administrative levels,including provincial,prefectural,county authorities,73 protection stations,and 13364 pastoralists residing within the PA.The findings show that stakeholders at the provincial level,as well as those from the Ali and Naqu prefectures and six counties,place greater emphasis on institutional and resource-related needs than on other categories(attention score:7.0-9.3 vs.5.0-7.0).In contrast,stakeholders from the 73 protection stations prioritize technological and capacity-building needs more strongly than other types(attention score:8.0-9.0 vs.4.0-8.0).The 13364 pastoralists assign the highest importance to social needs relative to other categories(attention score:9.0-9.5 vs.3.0-8.0).Most of the eight existing protection measures were found to indirectly support broader climate adaptation efforts.In particular,protective actions addressing fire,pests,and weather-related disasters can be classified as autonomous adaptation,while other measures generate outcomes that enhance adaptation capacity under specific conditions.Adaptation options,grouped into three main types and 13 subcategories,differ across stakeholder groups,although substantial overlap exists between these options and current protective actions,including ecosystem based adaptation strategies,adaptation-related practices,autonomous adaptation measures,and emergency interventions.Overall,these findings highlight the critical role of all stakeholders-especially staff from the 73 protection stations and the 13364 pastoralists-in the effective implementation of adaptation actions within the PA.
文摘Climate change poses a profound threat to mountain agro-ecosystems,particularly in the Himalayan region of West Bengal,India,by disrupting precipitation patterns,increasing temperature variability,and intensifying extreme weather events.Despite growing evidence of climate change impacts,there remains a critical research gap in understanding how socioeconomic factors drive farmers' adaptation strategies to climate change in this vulnerable region.This study examines how farmers in the Himalayan region of West Bengal,India,perceived and responded to the growing impacts of climate change on mountain agro-ecosystems.Drawing on cross-sectional data from 370 farm households selected through multistage sampling,the research employs a combination of analytical tools,including the severity index(SI) to assess farmers' perceptions to climate change,the adaptation index(AI) to evaluate adaptive responses,the Garrett's ranking technique to prioritize constraints,and the ordered logistic regression to identify key socioeconomic drivers of adaptation.Findings reveal a high level of climate awareness among farmers,particularly regarding the increase in weather extremes(SI=74.87%),increase in temperature(SI=72.31%),and irregular rainfall patterns and highly erratic rainfall(SI=62.52%).The most commonly adopted strategies include adopting intercropping and mixed cropping systems(AI=0.613),adoption of the integrated farming system model(AI=0.600),and shift towards non-farm employment(AI=0.608),while the adoption of climate-resilient crop varieties and improved irrigation remains limited.Regression analysis highlights that education(regression coefficient=0.38),average landholding size(regression coefficient=1.21),and access to daily weather forecast information(regression coefficient=1.92) significantly promote adaptive behaviour,whereas age(regression coefficient= –0.09) and gender(regression coefficient= –0.76) are negatively associated.Institutional constraints,particularly unavailability of institutional credit,emerge as primary barriers.The study underscores the urgent need for region-specific,inclusive policy frameworks that enhance climate advisory services,support technology dissemination,and empower marginalized groups in the Himalayan region of West Bengal.By fostering informed,equitable,and resilient agricultural systems,these strategies can significantly strengthen the adaptive capacity of mountain farming communities and contribute to sustainable development under a changing climate.
文摘1.Introduction The field of exercise science is experiencing a renaissance,with recent research illuminating the molecular,cellular,and systemic effects of physical activity.This is largely due to the now unequivocal evidence that a lack of physical activity,not only has direct effects on the prevalence of non-contagious diseases(NCDs)but has profound additive effects of other risk factors for NCD such as obesity and hypertension.1 The articles in this special topic of Journal of Sport and Health Science(JSHS)are dedicated to research on Exercise biochemistry&metabolism.
基金supported by Ho Chi Minh City Open University,Vietnam under grant number E2024.02.1CD and Suan Sunandha Rajabhat University,Thailand.
文摘The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.
基金supported by Science and Technology Program from the Forestry Administration of Guangdong Province(2024KJQT0012)the Guangdong Provincial Key R&D Program(2022B1111040001)+2 种基金the National Forestry Administration rare and endangered species field rescue and breeding project(Gui lin hu yu O10)the National Natural Science Foundation of China(32200337)a fellowship from the China Postdoctoral Science Foundation(2022M712003).
文摘Gibbons are small,arboreal apes that play a critical role in tropical biodiversity and ecosystem ecology.However,nearly all species of gibbons are threatened by habitat loss,illegal trade,hunting,and other human activities.Long-term poor understanding of their genetics and evolution undermines effective conservation efforts.In this study,we analyse comparative population genomic data of four Nomascus species.Our results reveal strong genetic differentiation and gene flow among Nomascus species.Additionally,we identify genomic features that are potentially related to natural selection linked to vocalization,fructose metabolism,motor balance,and body size,consistent with the unique phenotype and adaptability of gibbons.Inbreeding,coupled with population declines due to climate change and historical human activities,leads to reduced genetic diversity and the accumulation of deleterious variations that likely affect cardiovascular disease and the reproductive potential of gibbons and further reduce their fitness,highlighting the urgent need for effective conservation strategies.
基金supported by the 2024 Zhejiang Provincial Women’s Federation&Women’s Studies Association Research Project(202450).
文摘Objective:International students frequently face psychological adaptation difficulties while studying and living abroad.As an effective psychological resource,positive solitude has been identified as a potential factor for improving psychological well-being,but the underlying mechanism linking the two has not been fully explored.The current study aims to explore the relationship between positive solitude and psychological adaptation of international students,with particular emphasis on the intermediary roles of authenticity and loneliness.Methods:A total of 529 international tertiary students(Mage=23.76,SD=5.08;60.68%male)were surveyed using the Positive Solitude Scale(PSS),Authenticity Scale(AS),6-item UCLA Loneliness Scale(ULS-6),and Brief Psychological Adaptation Scale(BPAS).SPSS27.0 was used for descriptive statistical analysis and Pearson correlation analysis.PROCESS macro(Model 6)was employed to test a serial mediation model,in which authenticity and loneliness function as intermediary variables between positive solitude and psychological adaptation.Results:The correlation analysis indicated significant associations among positive solitude,authenticity,loneliness,and psychological adaptation(r=−0.544~0.511).Positive solitude was directly and positively related to psychological adaptation(β=0.132,t=3.609,p<0.001)and indirectly related to psychological adaptation through two pathways:a single mediation via authenticity(indirect effect=0.089)and a serial mediation through authenticity and loneliness(indirect effect=0.062).Loneliness did not serve as a significant mediator(indirect effect=–0.015,95%CI[–0.049,0.019]).The total indirect effect was 0.136.Conclusions:Interventions targeting international students’capacity for experiencing positive solitude and authenticity can help to reduce loneliness and increase psychological adaptation.The findings derived from this study are conducive to understanding the relationship between positive solitude and psychological adaptation,as well as its underlying mechanisms.In addition,the study offers a new perspective for educational management and psychological counseling services for international students.
文摘Conventionally,foundations have been classified as shallow or deep in routine civil engineering practice.However,due to recent developments,two other approaches,semi-deep and ground modification foundations,are now available,complicating foundation categorization.Accordingly,a new concept for foundation categorization is introduced in this paper based on insights into the theory of structure analysis.Based on the form aspect,foundation systems can be categorized as one-dimensional(linear),two-dimensional(planar),and threedimensional(volumetric).Based on the load transfer aspect,foundations can also be categorized as vector-acting(piles),section or surface-acting(rafts and shells),and block-acting(piled rafts).As a step toward implementing this new categorization scheme,a database of 22 cases has been compiled,symbolizing novel introduced foundation systems.This compilation involves structures such as offshore jackets,high-rise buildings,towers and storages,and diverse geomaterials.Among them,a few have been selected for detailed evaluation,emphasizing influential factors in foundation selection,comprising superstructure,subsoil condition,foundation system,circumferential conditions,and supplementary considerations,that is,constructional and sustainability-based issues.Lessons learned from experience and these knowledge-based cases have described for foundation selection and implementation.Geotechnical and practical aspects with critical components have been realized as major performance assessment and comparison factors.Foundation systems have been compared and ranked using the improved analytic hierarchy process approach.Finally,four categories of buildings,from low-rise to towers and four prevailing levels of soil strength,from soft to very hard,have been considered to propose a perspective for building substructure implementation,adapted via relevant cases.Overall,the introduced categorization is recognized as an efficient algorithm for the experimentation of appropriate foundations for specific structures and subsoil conditions.
基金funded by grants from the National Natural Science Foundation of China(32288101 and 91631306 to B.S32170632 and 32000390 to Y.H.32400503 to Y.G.)Major Scientific Project of Yunnan Province(202305AH340007 to B.S.)+4 种基金Yunnan Revitalization Talent Support Program Science&Technology Champion Project(202005AB160004 to B.S.)Yunnan Revitalization Talent Support Program Innovation Team(202405AS350008)Yunnan Scientist Workshops(to B.S.)the Youth Innovation Promotion Association of CAS(to Y.H.),the Science and Technology General Program of Yunnan Province(202301AW070010 and 202001AT070110 to Y.H.)and the Provincial Key Research,Development,and Translational Program(XZ202101ZY0009G to Baima.).
文摘Nitric oxide(NO)is a key vasodilator that regulates vascular pressure and blood flow.Tibetans have developed a"blunted"mechanism for regulating NO levels at high altitude,with GTP cyclohydrolase 1(GCH1)identified as a key candidate gene.Here,we present comprehensive genetic and functional analyses of GCH1,which exhibits strong Darwinian positive selection in Tibetans.We show that Tibetan-enriched GCH1 variants down-regulate its expression in the blood of Tibetans.Based on this observation,we generate the heterozygous Gch1 knockout(Gch1^(+/-))mouse model to simulate its downregulation in Tibetans.We find that under prolonged hypoxia,the Gch1^(+/-)mice have relatively higher blood NO and blood oxygen saturation levels compared with the wild-type(WT)controls,providing better oxygen supplies to the cardiovascular and pulmonary systems.Markedly,hypoxia-induced cardiac hypertrophy and pulmonary remodeling are significantly attenuated in the Gch1^(^(+/-))mice compared with the WT controls,likely due to the adaptive changes in molecular regulations related to metabolism,inflammation,circadian rhythm,extracellular matrix,and oxidative stress.This study sheds light on the role of GCH1 in regulating blood NO,contributing to the physiological adaptation of the cardiovascular and pulmonary systems in Tibetans at high altitude.
基金supported by China Hunan Provincial Science&Technology Department(No.2023NK2031)the Natural Science Foundation of Hunan Province(No.2023JJ40031)the Ministry of Human Resources and Social Security(No.H20240365).
文摘To explore the adaptive mechanisms of the partial nitritation-anammox(PNA)process under high salinity stress during kitchen wastewater treatment,focusing on their physiological and molecular responses through metagenomic analysis.An airlift inner-circulation partition bioreactor(AIPBR)was developed,featuring an inner cylinder and a flow guide tube to create distinct oxygen gradients,facilitating the study of microbial adaptation under varying salt conditions.The AIPBR was operated with synthetic wastewater containing ammonium concentrations of 1800±100 mg/L and salinity gradients ranging from 1 to 10 g/L,followed by a fixed salinity period at 6 g/L,with ammonium concentrations approximately 850 mg/L.High-throughput metagenomic analysis revealed shifts in functional genes and metabolic pathways in response to salinity stress.Anammox bacteria adapted by enriching genes involved in the synthesis of osmoprotective compounds and activating energy-producing pathways like the tricarboxylic acid cycle(TCA).These adaptations,along with modifications in membrane composition,were essential for sustaining system stability under elevated salinity.Under prolonged high salinity stress,anaerobic ammonium oxidizing(AnAOB)exhibited improved salt tolerance,maintaining a total nitrogen removal efficiency above 85%and stabilizing after an adaptation phase.The metagenomic data revealed a marked enrichment of genes associated with ion transport,stress response mechanisms,and DNA repair pathways.Changes in microbial community composition favored salt-tolerant species,supporting system stability.These findings highlight the applicability of the developed bioreactor for scaling up the PNA process to handle high-salinity wastewater,providing a promising avenue for sustainable nitrogen removal in challenging environments.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2020-NR049579).
文摘High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of labels.Moreover,an optimization problem that fully considers all dependencies between features and labels is difficult to solve.In this study,we propose a novel regression-basedmulti-label feature selectionmethod that integrates mutual information to better exploit the underlying data structure.By incorporating mutual information into the regression formulation,the model captures not only linear relationships but also complex non-linear dependencies.The proposed objective function simultaneously considers three types of relationships:(1)feature redundancy,(2)featurelabel relevance,and(3)inter-label dependency.These three quantities are computed usingmutual information,allowing the proposed formulation to capture nonlinear dependencies among variables.These three types of relationships are key factors in multi-label feature selection,and our method expresses them within a unified formulation,enabling efficient optimization while simultaneously accounting for all of them.To efficiently solve the proposed optimization problem under non-negativity constraints,we develop a gradient-based optimization algorithm with fast convergence.Theexperimental results on sevenmulti-label datasets show that the proposed method outperforms existingmulti-label feature selection techniques.
文摘Community-pioneered Nature-based Solutions(NbS)have become the main strategies in climate adaptation,although the evidence of their effectiveness and the governing conditions is still fragmented across hazards,ecosystems,and disciplines.The current review is a synthesis of the worldwide empirical research based on the concept of community-led NbS,meaning those interventions where communities have significant decision-making power and responsibility concerning the design,stewardship,sharing of benefits,and learning.On a taxonomy that differentiates between proximal ecosystem functionality and hazard modulation and distal human vulnerability reduction,and procedural,distributional,and recognition justice,we systematize the evidence-based findings according to hazardecosystem-intervention type(coastal storms and sea-level rise,flooding,drought and water insecurity,urban heat,and emerging compound risks)and we compare the outcomes.The results are reported to have the co-benefits of biodiversity gain,livelihood diversification,and better well-being,though they can be neutralized by elite capture,exclusion,tenure insecurity,as well as,in cities,green gentrification and displacement.The analysis of governance indicates repeating bundles related to longer-lasting and fairer results:hedge rights and tenure,community-enforceable and legitimizing representation institutions,financing institutions with longer horizons of maintenance and active adaptation,protection,and grievance,ethical supervision,and data governance.Our findings conclude that to scale community-led NbS,we need to switch the targets of areas to the target of governance quality and design of evaluation that would connect a change in the ecosystem to lived risk reduction and distributional change.
基金supported by the National Key Research and Development Plan of China(2021YFD2200202)the Key Research and Development Project of Jiangsu Province,China(BE2021366).
文摘Populus species,important economic species combining rapid growth with broad ecological adaptability,play a critical role in sustainable forestry and bioenergy production.In this study,we performed whole-genome resequencing of 707 individuals from a full-sib family to develop comprehensive single nucleotide polymorphism(SNP)markers and constructed a high-density genetic linkage map of 19 linkage groups.The total genetic length of the map reached 3623.65 cM with an average marker interval of 0.34 cM.By integrating multidimensional phenotypic data,89 quantitative trait loci(QTL)associated with growth,wood physical and chemical properties,disease resistance,and leaf morphology traits were identified,with logarithm of odds(LOD)scores ranging from 3.13 to 21.72 Notably,pleiotropic analysis revealed significant colocaliza and phenotypic variance explained between 1.7% and 11.6%.-tion hotspots on chromosomes LG1,LG5,LG6,LG8,and LG14,with epistatic interaction network analysis confirming genetic basis of coordinated regulation across multiple traits.Functional annotation of 207 candidate genes showed that R2R3-MYB and bHLH transcription factors and pyruvate kinase-encoding genes were significantly enriched,suggesting crucial roles in lignin biosynthesis and carbon metabolic pathways.Allelic effect analysis indicated that the frequency of favorable alleles associated with target traits ranged from 0.20 to 0.55.Incorporation of QTL-derived favorable alleles as random effects into Bayesian-based genomic selection models led to an increase in prediction accuracy ranging from 1% to 21%,with Bayesian ridge regression as the best predictive model.This study provides valuable genomic resources and genetic insights for deciphering complex trait architecture and advancing molecular breeding in poplar.
基金supported by the National Key R&D Program of China under Grant No.2022YFA1003701the Open Research Fund of Yunnan Key Laboratory of Statistical Modeling and Data Analysis,Yunnan University under Grant No.SMDAYB2023004。
文摘Quantile regression(QR)has become an important tool to measure dependence of response variable's quantiles on a number of predictors for heterogeneous data,especially heavy-tailed data and outliers.However,it is quite challenging to make statistical inference on distributed high-dimensional QR with missing data due to the distributed nature,sparsity and missingness of data and nondifferentiable quantile loss function.To overcome the challenge,this paper develops a communicationefficient method to select variables and estimate parameters by utilizing a smooth function to approximate the non-differentiable quantile loss function and incorporating the idea of the inverse probability weighting and the penalty function.The proposed approach has three merits.First,it is both computationally and communicationally efficient because only the first-and second-order information of the approximate objective function are communicated at each iteration.Second,the proposed estimators possess the oracle property after a limited number of iterations without constraint on the number of machines.Third,the proposed method simultaneously selects variables and estimates parameters within a distributed framework,ensuring robustness to the specified response probability or propensity score function of the missing data mechanism.Simulation studies and a real example are used to illustrate the effectiveness of the proposed methodologies.
基金Supported by Chongqing Health Commission and Chongqing Science and Technology Bureau,No.2023MSXM182。
文摘Rheumatoid arthritis(RA)patients face significant psychological challenges alongside physical symptoms,necessitating a comprehensive understanding of how psychological vulnerability and adaptation patterns evolve throughout the disease course.This review examined 95 studies(2000-2025)from PubMed,Web of Science,and CNKI databases including longitudinal cohorts,randomized controlled trials,and mixed-methods research,to characterize the complex interplay between biological,psychological,and social factors affecting RA patients’mental health.Findings revealed three distinct vulnerability trajectories(45%persistently low,30%fluctuating improvement,25%persistently high)and four adaptation stages,with critical intervention periods occurring 3-6 months postdiagnosis and during disease flares.Multiple factors significantly influence psychological outcomes,including gender(females showing 1.8-fold increased risk),age(younger patients experiencing 42%higher vulnerability),pain intensity,inflammatory markers,and neuroendocrine dysregulation(48%showing cortisol rhythm disruption).Early psychological intervention(within 3 months of diagnosis)demonstrated robust benefits,reducing depression incidence by 42%with effects persisting 24-36 months,while different modalities showed complementary advantages:Cognitive behavioral therapy for depression(Cohen’s d=0.68),mindfulness for pain acceptance(38%improvement),and peer support for meaning reconstruction(25.6%increase).These findings underscore the importance of integrating routine psychological assessment into standard RA care,developing stage-appropriate interventions,and advancing research toward personalized biopsychosocial approaches that address the dynamic psychological dimensions of the disease.
基金supported by the China Agriculture Research System of MOF and MARAthe National Natural Science Foundation of China (31872337 and 31501919)the Agricultural Science and Technology Innovation Project,China (ASTIP-IAS02)。
文摘The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2024-00406320)the Institute of Information&Communica-tions Technology Planning&Evaluation(IITP)-Innovative Human Resource Development for Local Intellectualization Program Grant funded by the Korea government(MSIT)(IITP-2026-RS-2023-00259678).
文摘Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional domain adaptation methods assume a single source domain,making them less suitable for modern deep learning settings that rely on diverse and large-scale datasets.To address this limitation,recent research has focused on Multi-Source Domain Adaptation(MSDA),which aims to learn effectively from multiple source domains.In this paper,we propose Efficient Domain Transition for Multi-source(EDTM),a novel and efficient framework designed to tackle two major challenges in existing MSDA approaches:(1)integrating knowledge across different source domains and(2)aligning label distributions between source and target domains.EDTM leverages an ensemble-based classifier expert mechanism to enhance the contribution of source domains that are more similar to the target domain.To further stabilize the learning process and improve performance,we incorporate imitation learning into the training of the target model.In addition,Maximum Classifier Discrepancy(MCD)is employed to align class-wise label distributions between the source and target domains.Experiments were conducted using Digits-Five,one of the most representative benchmark datasets for MSDA.The results show that EDTM consistently outperforms existing methods in terms of average classification accuracy.Notably,EDTM achieved significantly higher performance on target domains such as Modified National Institute of Standards and Technolog with blended background images(MNIST-M)and Street View House Numbers(SVHN)datasets,demonstrating enhanced generalization compared to baseline approaches.Furthermore,an ablation study analyzing the contribution of each loss component validated the effectiveness of the framework,highlighting the importance of each module in achieving optimal performance.
基金supported by the National Natural Science Foundation of China Funded Project(Project Name:Research on Robust Adaptive Allocation Mechanism of Human Machine Co-Driving System Based on NMS Features,Project Approval Number:52172381).
文摘To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.
